AIONA Lab
Artificial Intelligence for Optimization, Nutrition and Applied Health Laboratory
Our Research
Personal Nutrition Hypercube
How are nutrition, vital signs, and symptoms connected? Achieve better health with personalized insights and recommendations.
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Studyflow – Digital Research Platform
How can digital studies in the healthcare sector be conducted smoothly? With a GDPR-compliant study platform for mobile apps and health data.
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Image2Nutrition – From Image to Nutrition
How do you go from a photo of a meal to a corresponding list of ingredients, weight measurements and nutritional values?
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Pharmacokinetics
How are active ingredients absorbed, distributed, and metabolized in the body? AI models these relationships.
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Digital Hospital
How can processes be optimized? We develop solutions that make patients feel safer and reduce staff workload.
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Open Nutrition Dataset
How can the nutrient content of recipes and ingredients be determined? We develop a dataset for medicine and nutrition counseling.
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Quantum Reservoir and Data Selection (QuaRDS) – Merck
The project is developing novel quantum reservoir computing methods for the analysis of complex biomedical data. In collaboration with Merck, LMU is investigating the conditions under which QRC can offer advantages over classical methods and is developing new approaches for interpretable AI in personalized medicine and drug discovery.
Further Information
Bayesian Network Analysis and Inference via Quantum-enhanced Optimization (BAIQO) – Merck
The project develops innovative quantum algorithms to optimize clinical trials and helps make the development of new drugs faster, more efficient, and more targeted. The goal is to improve data-driven decision-making processes and enable patients to gain earlier access to new therapies.
Further Information
Analysis of Wearable Sensor Data for Sleep Quality
Pierre Franke, Nina Freise, Gerhard Stenzel, Claudia Linnhoff-Popien
Bachelor's Thesis
Development and Evaluation of Multi-Agent Extensions for Medical Reinforcement Learning Environments
Julian Kopetzky, Maximilian Zorn, Philipp Altmann, Claudia Linnhoff-Popien
Bachelor's Thesis
Uncertainty-Aware Modelling of Acute Respiratory Exposure to Chloropicrin
Minh Son Tran, Benedikt Paul, Gerhard Stenzel, Claudia Linnhoff-Popien
Bachelor's Thesis
Analysis and Classification of Sleep Quality Using Wearable Data
Yanik Bene, Nina Freise, Michael Kölle, Claudia Linnhoff-Popien
Bachelor's Thesis
Automated Identification of Leukemia Cells in Fluorescence In Situ Hybridization (FISH) Images
Ivan Marjanovic, Michael Kölle, Alexander Feist, Nina Freise, Claudia Linnhoff-Popien
Bachelor's Thesis
Skin Disease Classification Using Mobile Deep Learning Models
Abdulla Babayev, Alexander Klingebiel, Michael Kölle, Claudia Linnhoff-Popien
Bachelor's Thesis
A Comparative Evaluation of Rule and Pattern Mining Methods in Healthcare Data
Rosana Niebauer, Gerhard Stenzel, Alexander Klingebiel, Claudia Linnhoff-Popien
Bachelor's Thesis
Multi-Objective Model Optimization for Federated Learning in Medical Image Classification
Levon Boos, Jonas Stein, Leo Sünkel, Claudia Linnhoff-Popien
Bachelor's Thesis
Reliability Assessment of Retrieval Augmented Generation in Personalized Nutrition
Tim Lindner, Nina Freise, Gerhard Stenzel, Claudia Linnhoff-Popien
Master's Thesis
Beyond Tracking: Designing and Evaluating a Mobile Application to Foster Self-Reflection in a Nutritional Context
Lea Sigethy, Michael Kölle, Sebastian Wölckert, Claudia Linnhoff-Popien
Master's Thesis
Recipe and Nutrition Dataset from Real-World Data Using Large Language Models
Alexander Feist, Marcel Mohrmann, Alexia Pagkopoulou, Kilian Ziegler, Nina Freise, Alexander Klingebiel, Claudia Linnhoff-Popien
Academic Project
Explainable Nutrition Question Answering With Retrieval-Augmented Generation on Evidence-Based Guidelines
Philipp Stoll, Qingshi Liu, Emily Reichmann, Yun Huang, Nina Freise, Alexander Klingebiel, Claudia Linnhoff-Popien
Academic Project
Personalized Nutrition Analysis Using Hidden Markov Models
Nils Rothamel, Louisa Ullmann, Julia Straninger, Isabell Hans, Nina Freise, Alexander Klingebiel, Claudia Linnhoff-Popien
Academic Project
Personalized Time-Series Nutrition Analysis With Probabilistic Models and Large Language Model Explanations
Sara Juarez Oropeza, Abdullah Koukash, Anna Tsaan, Nick Thomas, Nina Freise, Alexander Klingebiel, Claudia Linnhoff-Popien
Academic Project
Real-Time On-Device Rehabilitation Feedback Using 3D Pose Estimation
Kristina Kuznetsova, Julian Geißinger, Yannic Kindermann, Yu Zhou, Nina Freise, Alexander Klingebiel, Claudia Linnhoff-Popien
Academic Project
Agentic Development Lifecycle under GDPR Constraints for Privacy-First Research Platforms
Laura Bengs, Gerhard Stenzel, Nina Freise, Claudia Linnhoff-Popien
Academic Project
Dimensionality Reduction with Autoencoders for Efficient Classification with Variational Quantum Circuits
Jonas Maurer, Michael Kölle, Philipp Altmann, Leo Sünkel, Claudia Linnhoff-Popien
Bachelor's Thesis
Impact of Different Quantum Noise Channels on Global Model Convergence and Robustness in Quantum Federated Learning
Paul Kreppold, Thomas Gabor, Leo Sünkel, Tobias Rohe, Claudia Linnhoff-Popien
Bachelor's Thesis
Ingredient Recognition and Relative Quantity Estimation in Food Images on Hardware-Constrained Systems
Michael Anderle, Gerhard Stenzel Michael Kölle, Thomas Gabor, Claudia Linnhoff-Popien
Master's Thesis
Anomaly Detection on Medical Images Using Classification of Clustering Results
Sebastian Haugg, Robert Müller, Michael Kölle, Claudia Linnhoff-Popien
Bachelor's Thesis
Publications
Quantum Boltzmann Machines Using Parallel Annealing for Medical Image Classification
IEEE Xplore 2025
Further Information
The Role of Domain-Specific Models for Synthetic Data Generation with Iterative Prompt Optimization
IEEE Xplore 2025
Further Information
IPROPS – Iterative Prompt Refinement for Optimizing Privacy-Preserving Synthetic Data Generation
IEEE Xplore 2025
Further Information
Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation
Springer Nature 2025
Further Information
Courses
Practical Course “Applied AI for Health”
The practical course teaches students how to analyze use cases in the field of “Applied AI for Health” (AI4H). Students develop intelligent systems that support decision-making processes in healthcare.
Seminar “AI for Health”
The seminar covers selected topics in artificial intelligence (AI) aimed specifically at healthcare applications. It focuses on innovative methods and technologies that improve medical diagnostics, therapy, and patient care.
Working Group “AI for Health”
The “AI for Health” working group explores the challenges and opportunities presented by artificial intelligence in healthcare. The group provides a platform for researchers, students, and interested parties to exchange ideas and collaborate. During regular meetings, we present current research projects and discuss scientific publications to analyze developments and trends. Topics include machine learning for analyzing health data, providing personalized recommendations for well-being, and addressing ethical issues in the sensitive context of healthcare.
Theses
We appreciate your interest in writing a thesis at the AIONA Lab. If you would like to write your thesis with us, please first visit our staff pages to learn about our research areas and current topics. Then, please use our inquiry form to submit your request.
